MATEC Web Conf.
Volume 252, 2019III International Conference of Computational Methods in Engineering Science (CMES’18)
|Number of page(s)||6|
|Section||Computational Artificial Intelligence|
|Published online||14 January 2019|
Application of neural network in determination of parameters for milling AZ91HP magnesium alloy with surface roughness constraint
Lublin University of Technology, Faculty of Management, Department of Organisation of Enterprise, Nadbystrzycka 38, 20-618 Lublin, Poland
2 Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering, Nadbystrzycka 36, 20-618 Lublin, Poland
* Corresponding author: email@example.com
This paper presents the model for milling AZ91HP magnesium alloy with TiAlN coated carbide end mill. The model was developed on the basis of experimental data from the neural network training data set. The milling process was conducted at constant parameters of tool geometry, workpiece strength properties, technological machine properties, radial and axial depth of cut. The range of changeable machining parameters specified in this study included cutting speed, feed per tooth, and the output variable: the arithmetical mean roughness parameter (Ra). The process was modelled by means of MatLab software and its Neural Network Toolbox. The developed model was implemented in the algorithm designed to determine optimal milling conditions, exploring the space of acceptable parameters in search of those which would meet the specified roughness parameter at maximum efficiency.
© The Authors, published by EDP Sciences, 2019
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